This transformation applies a Kalman filter to smooth data. Source: https://github.com/winedarksea/AutoTS/blob/master/autots/tools/transform.py#L3467
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Exploring how the use of genetic algorithms and continuous learning principles creates a system that can adapt to changing patterns over time as the dynamics of the forecast source data changes.
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The anomaly removal transformation removes outliers from source data before forecasting. Source: https://github.com/winedarksea/AutoTS/blob/master/autots/tools/transform.py#L2848
Nousot's demand forecasting solution splits your data into four major buckets in order to model each one separately.
This time series has a single dynamic that can be learned by a local model which only looks at the historic data to make future predictions.